LATENT FINGERPRINT MATCHING USING AUTOMATED FINGERPRINT IDENTIFICATION SYSTEM

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  • A Project on

    LATENT FINGERPRINT MATCHING USING AUTOMATED

    FINGERPRINT IDENTIFICATION SYSTEM

    Submitted in partial fulfillment of the requirements for the degree of

    Bachelor of Technology

    in

    Electronics and Communication Engineering

    by

    Manish Negi

    Pratiksha Yadav

    Shubham

    Rishi Raj Singh Rawat

    Under the guidance of

    Mr. Manoj Kumar

    DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING

    G. B. PANT ENGINEERING COLLEGE, PAURI, UTTARAKHAND, INDIA

    JUNE 2015

  • DECLARATION

    We hereby declare that this dissertation entitled LATENT FINGERPRINT MATCH-

    ING USING AUTOMATED FINGERPRINT IDENTIFICATION SYSTEM submitted

    to the Department of Electronics and Communication Engineering, G. B. Pant Engi-

    neering College, Pauri Garhwal (Uttarakhand) for the award of Bachelor of Technology

    degree in Electronics and Communication Engineering is a bonafide work carried out

    by us under the guidance of Mr. Manoj Kumar and that it has not been submitted

    anywhere for any award. Where other sources of information have been used, they have

    been acknowledged.

    Date: 08 June 2015 Manish Negi

    Place: GBPEC, Pauri Pratiksha Yadav

    Shubham

    Rishi Raj Singh Rawat

  • CERTIFICATE

    This is to certify that the dissertation entitled LATENT FINGERPRINT MATCHING

    USING AUTOMATED FINGERPRINT IDENTIFICATION SYSTEM being submit-

    ted by Manish Negi, Pratiksha Yadav, Shubham and Rishi Raj Singh Rawat in the

    partial fulfilment of the requirements for the award of Bachelor of Technology degree

    in Electronics and Communication Engineering to the Department of Electronics and

    Communication Engineering, G. B. Pant Engineering College, Pauri Garhwal (Uttarak-

    hand) is a bonafide work carried out by them under my guidance and supervision.

    To the best of my knowledge, the matter embodied in the dissertation has not been

    submitted for the award of any other degree or diploma.

    Date: 08 June 2015 Mr. Manoj Kumar

    Place: GBPEC, Pauri Assistant Professor

    SUPERVISOR

  • PREFACE

    Among all the biometric techniques, fingerprint-based identification is the oldest method

    which has been successfully used in numerous applications. Everyone has unique, im-

    mutable fingerprints. Identifying suspects based on impressions of fingers lifted from

    crime scenes (latent prints) is a routine procedure that is extremely important to foren-

    sics and law enforcement agencies. Latents are partial fingerprints that are usually

    smudgy, with small area and containing large distortion. Due to these characteristics,

    latents have a significantly smaller number of minutiae points compared to full (rolled

    or plain) fingerprints.

    A fingerprint is made of a series of ridges and furrows on the surface of the finger. The

    uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as

    well as the minutiae points. Minutiae points are local ridge characteristics that occur

    at either a ridge bifurcation or a ridge ending. Minutiae are very important features for

    fingerprint representation, and most practical fingerprint recognition systems store only

    the minutiae template in the database for further usage.

  • ACKNOWLEDGEMENT

    We place on record and warmly acknowledge the continuous encouragement, invaluable

    supervision, timely suggestions and inspired guidance offered by our guide Mr. Manoj

    Kumar, Assistant Professor,Department of Electronics & Communication Engineering,

    G. B. Pant Engineering College, Pauri Garhwal (Uttarakhand) in bringing this project

    to a successful completion. We are also grateful to Dr. Y. Singh, Head and Professor,

    Electronics & Communication Engineering Department and Dr. A. K. Gautam, As-

    sociate Professor, Electronics & Communication Engineering Department, G. B. Pant

    Engineering College, Pauri Garhwal (Uttarakhand) for helping us through the entire

    duration of the project. Last but not the least we express our sincere thanks to all our

    friends who have patiently extended all kind of help for accomplishing this undertaking.

    Our sincere thanks and acknowledgements are due to all our family members who have

    constantly encouraged us for completing this project.

    Manish Negi

    Pratiksha Yadav

    Shubham

    Rishi Raj Singh Rawat

  • ABSTRACT

    In this project, we propose a new fingerprint matching algorithm which is especially

    designed for matching latents. The proposed algorithm uses a robust alignment algo-

    rithm (local based descriptor MCC) to align fingerprints and measure similarities be-

    tween fingerprints by considering both minutiae and orientation field information. The

    conventional methods that utilize minutiae information treat them as a point set and

    find the matched points from different minutiae sets. These minutiae are used for fin-

    gerprint recognition, in which the fingerprints orientation field is reconstructed from

    virtual minutiae and further utilized in the matching stage to enhance the systems per-

    formance. A decision fusion scheme is used to combine the reconstructed orientation

    field matching with conventional minutiae based matching. Since orientation field is an

    important global feature of fingerprints, the proposed method can obtain better results

    than conventional methods. In our project it is implemented using MATLAB-GUI where

    virtual minutiae are considered.

  • CONTENTS

    Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

    Certificate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

    Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

    Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

    Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

    1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

    1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

    1.4 Fingerprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

    1.5 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

    1.5.1 Minutiae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

    1.5.2 Orientation field . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

    1.6 Need For Automated Extraction System . . . . . . . . . . . . . . . 6

    1.7 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2. FINGERPRINT ENHANCEMENT TECHNIQUE . . . . . . . . . . . 9

    2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    2.2 Binarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    2.2.1 Thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

    2.3 Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

  • 3. FEATURE EXTRACTION . . . . . . . . . . . . . . . . . . . . . . . . . . 12

    3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

    3.2 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

    3.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

    3.4 Orientation Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

    3.5 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

    4. DATABASE AND FINGERPRINT MATCHING . . . . . . . . . . . . 21

    4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

    4.2 Database FVC2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

    4.3 Fingerprint Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

    4.3.1 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

    4.3.2 Similarity Measure . . . . . . . . . . . . . . . . . . . . . . . . . . 25

    5. IMPLEMENTATION OF THE PROPOSED ALGORITHM . . . . . 26

    5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    5.2 Enhancement of the fingerprint image . . . . . . . . . . . . . . . . 26

    5.3 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

    5.3.1 Ridge Bifurcation . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

    5.3.2 Minutiae Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

    5.3.3 False Minutiae Removal . . . . . . . . . . . . . . . . . . . . . . . 30

    5.4 Orientation field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

    5.4.1 Segmentation and Region of interest . . . . . . . . . . . . . . . . 33

    5.5 Minutiae Match . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

    6. RESULT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

    6.1 Result and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . 35

    7. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

    vii

  • 8. APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

    8.1 Matlab Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

    REFEREN

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